By Steve Waters
The online age has brought a raft of benefits to investors. Nowadays, you can access a monumental pool of information. An incredible array of statistics and details that illustrate the state of markets.
You can go from large, big-data summaries down to minute details of individual property sale prices and descriptions.
This might seem great to the casual observer, however all this information can create a deafening roar of figures which could potentially cloud your judgement and lead to bad decisions.
So, when does the data become a hindrance rather than a help?
The availability of data and statistics means everyone can form an opinion and then hang that opinion upon a tabulated set of results.
Unfortunately, there’s a huge margin for error in just glancing at a percentage and assuming you know all the answers.
I believe the problem is two-fold.
Firstly, it’s in data itself. While stats are great, they are simply a page of numbers. There’s no degree of detail. The digits are just the result of multiple influences interacting. Sadly, users often fail to recognise that the outcome is less important than the process which brought it about.
This brings me to the second hurdle with data – the skill of interpretation. Unless you have enough experience to understand the process of how a set of results came to be, you aren’t reading them properly.
The ability to cut through the coal and unearth the diamonds is valuable – for the unskilled, however, the mistakes can be costly.
Being able to just recognise anomalies is, unfortunately, a half-way measure that can lead to trouble for amateur sleuths. Understanding why those anomalies occur is where skilled investors really make a profit (or avoid a loss).
When you notice a blip in the data that piques your interest, such as an uncharacteristically high capital growth rate or a tighter price discounting percentage, you must ask the all-important question, ‘Why?’.
Here’s a simple example – say the median house price in the imaginary location of Smith St, Jonesvale is $400,000 in 2017.
Then in 2018, it’s $300,000. What’s happened here? You can’t actually tell that from the stated 25 per cent fall in median values, can you.
Do your on-the-ground research. Canvass local agents and you’ll discover there were three house sales in the street where two had extreme white ant problems and the other was an inter-family transaction.
Suddenly it makes sense. It was an abnormality. The devil was in the detail.
When we, as professional advisors, gather data for interpretation, we approach it from multiple angles.
We start with macro-elements of national, state and regional numbers absorbing everything we can about property and the economy. This paints a broad picture of what’s happening.
We use multiple data sources and combine them to look for anomalies out of our newly created data sample. We tackle supply and demand, vacancies, growth rates, yields, discounting and returns.
We then head toward the micro-elements including suburb, sometimes down to street level, looking at the same sets of metrics.
You also need to dig deeper on the qualitative stuff. Speak to local agents, property managers, council members, town planners and others.
This is where you get your answers.
Here’s a pro tip too – what’s often most important is the trend, not the result.
So, use the same source anytime your comparing data over a time period and track the trends, not the absolute figure. If a median price is $1.3 million, that doesn’t say much. But if a median price has reduced by three per cent per quarter for the past four quarters – that might be a story.
While there are probably multiple hard-to-spot reasons why data might give an unreliable outcome, there are a few common traps worth looking for.
Firstly – check who’s supplying the data. Use confirmed analytics where possible, not speculation or prediction from unreliable sources.
Next – gather data from a range of sources but be aware, each data house can apply its own rules on how they calculate figures. For example, a suburb’s ‘median house price’ from one data source may include semi-detached units. This is despite the fact another data source doesn’t include them in the ‘house price’ measure.
Next, check the sample size. If you’re looking at annual average results for suburb, but they’re only based off three sales in the year, the outcomes will be highly unrealistic. The larger the sample, the more reliable the result.
Thirdly – try and be detailed about what you’re seeking. Not all property is the same and comparing and contrasting ‘like with like’ helps sharpen the outcome. Are you studying results for houses, units, townhouses, duplexes or development sites? If it’s houses, can you break it down by number of bedrooms, bathrooms and size of car accommodation? Keeping your sample consistent and defined helps boost the integrity of the result.
Also – be cautious of locational influences. For example, is there a high number of serviced apartments or short-stay student hubs in the suburb? If so, expect your rental and vacancy rates to be extraordinary compared to more traditional rental markets.
Lastly, be aware of when data was sourced and correlated. The more recent, the better. Delayed data will tell you about the past, but the further back it goes, the tougher it is to draw conclusions about your present and potential future.
If you are keen to learn more about data interpretation, that’s great. I would encourage you, in the initial stages, to keep it as simple as possible. Break it down into basics and then spend some time ‘on the ground’ in your areas of interest so you can confirm the actual lay of the land against the numbers you’ve analysed.
Also – data interpretation can do your head in and become a whirlpool of figures from which there is no escape. Ensure you don’t get a bad case of analysis paralysis by taking on so much data that you feel mentally drained.
Failing all else – rely on an expert. Learning to interpret figures effectively takes time and experience. If you feel overwhelmed, outsource it. Just contact a skilled professional and let them tackle the tough work.